
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis'/'Sensitivity')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)


from platform import python_version 
print(python_version())

3.8.8


import PV_ICE
PV_ICE.__version__

'v0.2.0+470.g2ad59aa'


MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')


#load in a baseline and materials for modification
sim1 = PV_ICE.Simulation(name='sim1', path=testfolder)

sim1.createScenario(name='PV_ICE', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['PV_ICE'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\Sensitivity
Baseline folder directed to default:  C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\baselines


timeshift = 2022-1995


#life sensitivity
#mod_reliability_t50, mod_reliability_t90, mod_degradation, mod_lifetime

sim1.createScenario(name='life_high', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['life_high'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim1.modifyScenario('life_high', 'mod_degradation', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']-0.1, start_year=2022) #standard current degrdation
#T50
sim1.modifyScenario('life_high', 'mod_reliability_t50',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']+10, start_year=2022)
#t90
sim1.modifyScenario('life_high', 'mod_reliability_t90',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']+10, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('life_high', 'mod_lifetime',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']+10, start_year=2022) #project lifetime of 25 years

#--------------------------------------------------------------------------------------

#life low
sim1.createScenario(name='life_low', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['life_low'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim1.modifyScenario('life_low', 'mod_degradation', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']+0.1, start_year=2022) #standard current degrdation
#T50
sim1.modifyScenario('life_low', 'mod_reliability_t50',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']-10, start_year=2022)
#t90
sim1.modifyScenario('life_low', 'mod_reliability_t90',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']-10, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('life_low', 'mod_lifetime',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']-10, start_year=2022) #project lifetime of 25 years


sim1.createScenario(name='eff_high', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['eff_high'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
sim1.modifyScenario('eff_high', 'mod_eff', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']+5, start_year=2022) #

#-------------------------------------------------------------------------------------------------------

sim1.createScenario(name='eff_high_bifi', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['eff_high_bifi'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
sim1.modifyScenario('eff_high_bifi', 'mod_eff', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']+5, start_year=2022) #

#------------------------------------------------------------------------------------------------------

sim1.createScenario(name='eff_low', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['eff_low'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
sim1.modifyScenario('eff_low', 'mod_eff', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']-5, start_year=2022) #


mod_circ_vars = ['mod_EOL_collection_eff', 'mod_EOL_pg4_recycled', 'mod_EOL_pb4_recycled']
mat_circ_vars = ['mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff', 'mat_MFG_scrap_Recycled_into_HQ',
                 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 'mat_PG4_Recycling_target', 'mat_Recycling_yield',
                 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG']


sim1.createScenario(name='circ_high', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['circ_high'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
for var in range(0,len(mod_circ_vars)):
    sim1.modifyScenario('circ_high', mod_circ_vars[var], 100.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim1.scenario['circ_high'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],100.0, start_year=2022) #

#-----------------------------------------------------------------------------------------------------------

sim1.createScenario(name='circ_low', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['circ_low'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
for var in range(0,len(mod_circ_vars)):
    sim1.modifyScenario('circ_low', mod_circ_vars[var],0.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim1.scenario['circ_low'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],0.0, start_year=2022) #


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim1.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE glass : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silicon : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silver : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE copper : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  life_high
life_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
life_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
life_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
life_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
life_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
life_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
life_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
life_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  life_low
life_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
life_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
life_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
life_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
life_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
life_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
life_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
life_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_high
eff_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_high_bifi
eff_high_bifi glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_low
eff_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_high
circ_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_low
circ_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)
global_projection.iloc[-1,:]

#fig, ax1 = plt.subplots()

#ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
#ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
#ax1.set_ylim(0,90)
#ax1.set_xlim(2000,2100)
#ax2 = ax1.twinx()
#ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
#ax2.set_ylabel('Annual Installations [TW]')
#ax2.set_ylim(0,5)
#plt.show()

#fig.savefig('energyresults-deployment.png', dpi=300, bbox_inches='tight')

World_cum              85966000.0
World_annual_[MWdc]      219320.0
Name: 2100, dtype: float64


#deployment projection for all scenarios
sim1.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)


sim1.scenario.keys()

dict_keys(['PV_ICE', 'life_high', 'life_low', 'eff_high', 'eff_high_bifi', 'eff_low', 'circ_high', 'circ_low'])


bifiFactors = {'PV_ICE':0.0,
               'life_high':0.0,
               'life_low': 0.0,
               'eff_high': 0.0,
               'eff_high_bifi': 0.9,
               'eff_low': 0.0,
               'circ_high':0.0,
               'circ_low': 0.0
              } 

#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
idx_temp = pd.RangeIndex(start=2000,stop=2101,step=1) #create the index
df_temp = pd.DataFrame(index=idx_temp, columns=['bifi'], dtype=float)
bifiPathDict={}

for f in bifiFactors.keys(): #loop over module types
    bifi = df_temp.copy() #copy of df
    bifi['bifi'] = bifiFactors[f] #assign column
    bifipath = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi.to_csv(path_or_buf=bifipath, index_label='year') #create file
    bifiPathDict[str(f)] = bifipath
    #append bifi path to dict? or list?


#CALCULATE MASS FLOWs
#13 scenarios, 7 mats = 

for scen in sim1.scenario.keys(): #loop over scenarios
    sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_high
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_low
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high_bifi
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_low
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_high
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_high  =  2787030969.8975344  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_high  =  92130764.51066588  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_high  =  522204.79505684547  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_high  =  315960505.1026145  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_high  =  1904912.986497598  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_high  =  185857396.31027597  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_high  =  78240039.20964287  tonnes.
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_low
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


#sim1.calculateMassFlow()


ii_yearly, ii_cumu = sim1.aggregateResults() #have to do this to get auto plots
sim1.saveSimulation(customname='_EnergySensitivity_identicalinstalls')

:) Saved Input and Output Dataframes


effective_capacity = ii_yearly.filter(like='ActiveCapacity')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=sim1.scenario.keys())
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


sim1.scenario['eff_high'].dataIn_m


#plt.plot(sim1.scenario['eff_high'].dataOut_m['Yearly_Sum_Area_PathsBad'])
#plt.plot(sim1.scenario['eff_high'].dataOut_m['Cumulative_Active_Area'])
plt.plot(sim1.scenario['eff_high'].dataOut_m['Area']/1e10, label='eff_high')

#plt.plot(sim1.scenario['eff_high_bifi'].dataOut_m['Yearly_Sum_Area_PathsBad'])
#plt.plot(sim1.scenario['eff_high_bifi'].dataOut_m['Cumulative_Active_Area'])
plt.plot(sim1.scenario['eff_high_bifi'].dataOut_m['Area']/1e10, label='eff_high_bifi')

plt.plot(sim1.scenario['PV_ICE'].dataOut_m['Area']/1e10, label='PVICE')


plt.plot(sim1.scenario['eff_high_bifi'].dataIn_m['new_Installed_Capacity_[MW]']/1e6, label='eff_high_bifi_installs')
plt.plot(sim1.scenario['PV_ICE'].dataIn_m['new_Installed_Capacity_[MW]']/1e6, label='PVICE_installs')
plt.legend()

<matplotlib.legend.Legend at 0x20506ba1a60>


UnderInstall_df = pd.DataFrame()


for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim1.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Effective_Capacity_[W]'][row])/1e6)  # MWATTS
        sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        UnderInstall_df.loc[row,scen] = Under_Installment #save the underinstallment as df
        #calculate flows for that scenario with it's bifi factor and modified weibull
        sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

sim1.calculateEnergyFlow()

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
life_high
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_high
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
life_low
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_low
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
eff_high
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
eff_high_bifi
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high_bifi
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


sim1.saveSimulation(customname='_EnergySensitivity_withreplacements')

#aggregate results - mass
cc_yearly, cc_cumu = sim1.aggregateResults() #have to do this to get auto plots
cc_yearly.to_csv(os.path.join(testfolder, 'cc_10scen_yearly.csv'))
cc_cumu.to_csv(os.path.join(testfolder, 'cc_10scen_cumu.csv'))

#aggregate results - energy
allenergy, energyGen, energy_demands = sim1.aggregateEnergyResults()
allenergy.to_csv(os.path.join(testfolder, 'cc_10scen_allenergy.csv'))
energyGen.to_csv(os.path.join(testfolder, 'cc_10scen_energyGen.csv'))
energy_demands.to_csv(os.path.join(testfolder, 'cc_10scen_energy_demands.csv'))

UnderInstall_df.to_csv(os.path.join(testfolder, 'cc_10scen_underInstalls.csv'))

:) Saved Input and Output Dataframes

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)


cumu_installs = cc_cumu.filter(like='newInstalled')
total_installed = cumu_installs.loc[2100]
total_installed.index= scennames_labels

plt.bar(scennames_labels, total_installed/1e6)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Installs with Replacements')


cumu_area_deployed = pd.DataFrame()

for scen in sim1.scenario.keys():
    area_deploy = sim1.scenario[scen].dataOut_m['Area'].cumsum()
    cumu_area_deployed = pd.concat([cumu_area_deployed,area_deploy], axis=1)
    
cumu_area_deployed.columns = sim1.scenario.keys()
cumu_area_deployed_total = cumu_area_deployed.iloc[-1,:]
#cumu_area_deployed

plt.bar(scennames_labels, cumu_area_deployed.iloc[-1,:]/1e12)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed Area [m2 1e12]')
plt.title('Cumulative Area Deployed with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Area Deployed with Replacements')


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
virgin = cumu_virgin_module.loc[2100]
virgin.index= scennames_labels

plt.bar(scennames_labels, virgin/1e9)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
#plt.xticks(rotation=90)

Text(0, 0.5, 'Virgin Material Requirements\n[billion tonnes]')


cumu_lifecycle_wastes = cc_cumu.filter(like='WasteAll_Module')
wastes = cumu_lifecycle_wastes.loc[2100]
wastes.index= scennames_labels

plt.bar(scennames_labels, wastes/1e9)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[billion tonnes]')

Text(0, 0.5, 'Lifecycle Wastes\n[billion tonnes]')


e_annual_sumDemands = energy_demands.filter(like='demand_total')
e_annual_sumDemands_cumu = e_annual_sumDemands.cumsum()
cumu_e_demands = e_annual_sumDemands_cumu.loc[2100]
cumu_e_demands.index= scennames_labels

plt.bar(scennames_labels, cumu_e_demands/1e12)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')

Text(0, 0.5, 'Cumulative Energy Demands\n[TWh]')


e_demands_eff_high = energy_demands.filter(like='eff_high')
e_demands_eff_high_cumu = e_demands_eff_high.cumsum()

e_demands_eff_high_cumu

#plt.bar(e_demands_eff_high_cumu.columns, e_demands_eff_high_cumu.iloc[-1,:])


energyGen_cumu = energyGen.cumsum()
energyGen_cumu.columns = e_annual_sumDemands_cumu.columns = scennames_labels
netEnergy_cumu = energyGen_cumu.loc[[2100]] - e_annual_sumDemands_cumu.loc[[2100]]
#netEnergy_cumu.index= scennames_labels

plt.bar(scennames_labels, netEnergy_cumu.loc[2100]/1e12)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')

Text(0, 0.5, 'Cumulative Net Energy [TWh]')


netEnergy_cumu_norm = netEnergy_cumu/netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall = netEnergy_cumu_norm-1

plt.bar(scennames_labels, netEnergy_cumu_norm_waterfall.loc[2100])
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)
plt.xticks(rotation=90)

([0, 1, 2, 3, 4, 5, 6, 7],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


energyBalance_allyears = energyGen_cumu/e_annual_sumDemands_cumu
energyBalance = energyBalance_allyears.loc[2100]
energyBalance.index = scennames_labels

plt.bar(scennames_labels, energyBalance)
plt.title('Energy Balance')
plt.ylabel('Unitless')

Text(0, 0.5, 'Unitless')


discussTable = pd.concat([total_installed,cumu_area_deployed_total,virgin,wastes,
                          cumu_e_demands, netEnergy_cumu.loc[2100].T, energyBalance],
                         axis=1, keys=['replacements','area','virgin','wastes','energydemand','netenergy','energybalance'])
#discussTable.to_csv(os.path.join(testfolder,'discussiontable.csv'))
discussTable


discussTable_norm = (discussTable/discussTable.loc['PV_ICE'])*100-100
discussTable_norm


#load in a baseline and materials for modification
sim2 = PV_ICE.Simulation(name='sim1', path=testfolder)

sim2.createScenario(name='PV_ICE', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['PV_ICE'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\Sensitivity
Baseline folder directed to default:  C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\baselines


mod_circ_vars = ['mod_EOL_collection_eff', 'mod_EOL_pg4_recycled', 'mod_EOL_pb4_recycled']
mat_circ_vars = ['mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff', 'mat_MFG_scrap_Recycled_into_HQ',
                 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 'mat_PG4_Recycling_target', 'mat_Recycling_yield',
                 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG']


#Life+Eff

sim2.createScenario(name='good_eff_life', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['good_eff_life'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim2.modifyScenario('good_eff_life', 'mod_degradation', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']-0.1, start_year=2022) #standard current degrdation
#T50
sim2.modifyScenario('good_eff_life', 'mod_reliability_t50',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']+10, start_year=2022)
#t90
sim2.modifyScenario('good_eff_life', 'mod_reliability_t90',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']+10, start_year=2022) 
#Mod Project Lifetime
sim2.modifyScenario('good_eff_life', 'mod_lifetime',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']+10, start_year=2022) #

sim2.modifyScenario('good_eff_life', 'mod_eff', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']+5, start_year=2022) #


#Eff+Circ
sim2.createScenario(name='good_eff_circ', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['good_eff_circ'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

for var in range(0,len(mod_circ_vars)):
    sim2.modifyScenario('good_eff_circ', mod_circ_vars[var], 100.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim2.scenario['good_eff_circ'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],100.0, start_year=2022) #

sim2.modifyScenario('good_eff_circ', 'mod_eff', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']+5, start_year=2022) #


#Life+Circ

sim2.createScenario(name='good_life_circ', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['good_life_circ'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim2.modifyScenario('good_life_circ', 'mod_degradation', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']-0.1, start_year=2022) #standard current degrdation
#T50
sim2.modifyScenario('good_life_circ', 'mod_reliability_t50',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']+10, start_year=2022)
#t90
sim2.modifyScenario('good_life_circ', 'mod_reliability_t90',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']+10, start_year=2022) 
#Mod Project Lifetime
sim2.modifyScenario('good_life_circ', 'mod_lifetime',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']+10, start_year=2022) #

for var in range(0,len(mod_circ_vars)):
    sim2.modifyScenario('good_life_circ', mod_circ_vars[var], 100.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim2.scenario['good_life_circ'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],100.0, start_year=2022) #


#life eff
sim2.createScenario(name='bad_life_eff', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['bad_life_eff'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim2.modifyScenario('bad_life_eff', 'mod_degradation', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']+0.1, start_year=2022) #standard current degrdation
#T50
sim2.modifyScenario('bad_life_eff', 'mod_reliability_t50',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']-10, start_year=2022)
#t90
sim2.modifyScenario('bad_life_eff', 'mod_reliability_t90',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']-10, start_year=2022) 
#Mod Project Lifetime
sim2.modifyScenario('bad_life_eff', 'mod_lifetime',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']-10, start_year=2022) #project lifetime of 25 years
#eff -5
sim2.modifyScenario('bad_life_eff', 'mod_eff', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']-5, start_year=2022) #


#eff circ
sim2.createScenario(name='bad_eff_circ', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['bad_eff_circ'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

sim2.modifyScenario('bad_life_eff', 'mod_eff', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']-5, start_year=2022) #    

for var in range(0,len(mod_circ_vars)):
    sim2.modifyScenario('bad_eff_circ', mod_circ_vars[var],0.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim2.scenario['bad_eff_circ'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],0.0, start_year=2022) #


#life circ
sim2.createScenario(name='bad_life_circ', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['bad_life_circ'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

#degradation rate:
sim2.modifyScenario('bad_life_circ', 'mod_degradation', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']+0.1, start_year=2022) #standard current degrdation
#T50
sim2.modifyScenario('bad_life_circ', 'mod_reliability_t50',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']-10, start_year=2022)
#t90
sim2.modifyScenario('bad_life_circ', 'mod_reliability_t90',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']-10, start_year=2022) 
#Mod Project Lifetime
sim2.modifyScenario('bad_life_circ', 'mod_lifetime',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']-10, start_year=2022)
    
#0 circ
for var in range(0,len(mod_circ_vars)):
    sim2.modifyScenario('bad_life_circ', mod_circ_vars[var],0.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim2.scenario['bad_life_circ'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],0.0, start_year=2022) #


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim2.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE glass : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silicon : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silver : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE copper : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  good_eff_life
good_eff_life glass : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life silicon : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life silver : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life copper : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  good_eff_circ
good_eff_circ glass : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ silver : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ copper : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  good_life_circ
good_life_circ glass : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ silver : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ copper : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  bad_life_eff
bad_life_eff glass : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff silicon : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff silver : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff copper : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  bad_eff_circ
bad_eff_circ glass : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ silver : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ copper : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  bad_life_circ
bad_life_circ glass : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ silver : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ copper : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)
global_projection.iloc[-1,:]

#fig, ax1 = plt.subplots()

#ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
#ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
#ax1.set_ylim(0,90)
#ax1.set_xlim(2000,2100)
#ax2 = ax1.twinx()
#ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
#ax2.set_ylabel('Annual Installations [TW]')
#ax2.set_ylim(0,5)
#plt.show()

#fig.savefig('energyresults-deployment.png', dpi=300, bbox_inches='tight')

World_cum              85966000.0
World_annual_[MWdc]      219320.0
Name: 2100, dtype: float64


#deployment projection for all scenarios
sim2.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)


sim2.scenario.keys()

dict_keys(['PV_ICE', 'good_eff_life', 'good_eff_circ', 'good_life_circ', 'bad_life_eff', 'bad_eff_circ', 'bad_life_circ'])


bifiFactors = {'PV_ICE':0.0,
               'good_eff_life':0.9, 
               'good_eff_circ': 0.9,
               'good_life_circ': 0.0,
               'bad_life_eff': 0.0,
               'bad_eff_circ':0.0,
               'bad_life_circ': 0.0
              } 

#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
idx_temp = pd.RangeIndex(start=2000,stop=2101,step=1) #create the index
df_temp = pd.DataFrame(index=idx_temp, columns=['bifi'], dtype=float)
bifiPathDict={}

for f in bifiFactors.keys(): #loop over module types
    bifi = df_temp.copy() #copy of df
    bifi['bifi'] = bifiFactors[f] #assign column
    bifipath = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi.to_csv(path_or_buf=bifipath, index_label='year') #create file
    bifiPathDict[str(f)] = bifipath
    #append bifi path to dict? or list?


#CALCULATE MASS FLOW= 

for scen in sim2.scenario.keys(): #loop over scenarios
    sim2.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_eff_life
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_eff_circ
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  good_eff_circ  =  2310622547.3601646  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  good_eff_circ  =  76377157.33952625  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  good_eff_circ  =  432998.5467168738  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  good_eff_circ  =  261938330.89069155  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  good_eff_circ  =  1578975.0110904214  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  good_eff_circ  =  154079973.9523989  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  good_eff_circ  =  64842935.423969  tonnes.
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_life_circ
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  good_life_circ  =  2786355639.833216  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  good_life_circ  =  92106694.69215429  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  good_life_circ  =  521715.7825075759  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  good_life_circ  =  315879580.16664696  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  good_life_circ  =  1904326.337037242  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  good_life_circ  =  185809720.8861563  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  good_life_circ  =  78211148.46289065  tonnes.
>>>> Calculating Material Flows <<<<

Working on Scenario:  bad_life_eff
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  bad_eff_circ
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  bad_life_circ
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


#sim1.calculateMassFlow()


ii_yearly2, ii_cumu2 = sim2.aggregateResults() #have to do this to get auto plots
sim2.saveSimulation(customname='_EnergySensitivity-goodbad_identicalinstalls')

:) Saved Input and Output Dataframes


effective_capacity = ii_yearly2.filter(like='ActiveCapacity')

plt.plot(ii_cumu2['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')

plt.plot(effective_capacity/1e6, label=sim2.scenario.keys())

plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


UnderInstall_df = pd.DataFrame()


for row in range (0,len(sim2.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim2.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim2.scenario[scen].dataOut_m['Effective_Capacity_[W]'][row])/1e6)  # MWATTS
        sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        UnderInstall_df.loc[row,scen] = Under_Installment #save the underinstallment as df
        #calculate flows for that scenario with it's bifi factor and modified weibull
        sim2.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

sim2.calculateEnergyFlow()

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
good_eff_life
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_eff_life
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
good_eff_circ
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_eff_circ
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  good_eff_circ  =  2310622547.3601646  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  good_eff_circ  =  76377157.33952625  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  good_eff_circ  =  432998.5467168738  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  good_eff_circ  =  261938330.89069155  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  good_eff_circ  =  1578975.0110904214  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  good_eff_circ  =  154079973.9523989  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  good_eff_circ  =  64842935.423969  tonnes.
good_life_circ
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_life_circ
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  good_life_circ  =  2786355639.833216  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  good_life_circ  =  92106694.69215429  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  good_life_circ  =  521715.7825075759  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  good_life_circ  =  315879580.16664696  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  good_life_circ  =  1904326.337037242  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  good_life_circ  =  185809720.8861563  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  good_life_circ  =  78211148.46289065  tonnes.
bad_life_eff
>>>> Calculating Material Flows <<<<

Working on Scenario:  bad_life_eff
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


sim2.saveSimulation(customname='_EnergySensitivity_withreplacements')

#aggregate results - mass
cc_yearly2, cc_cumu2 = sim2.aggregateResults() #have to do this to get auto plots
cc_yearly2.to_csv(os.path.join(testfolder, 'cc_10scen_yearly.csv'))
cc_cumu2.to_csv(os.path.join(testfolder, 'cc_10scen_cumu.csv'))

#aggregate results - energy
allenergy2, energyGen2, energy_demands2 = sim2.aggregateEnergyResults()
allenergy2.to_csv(os.path.join(testfolder, 'cc_10scen_allenergy.csv'))
energyGen2.to_csv(os.path.join(testfolder, 'cc_10scen_energyGen.csv'))
energy_demands2.to_csv(os.path.join(testfolder, 'cc_10scen_energy_demands.csv'))

#UnderInstall_df.to_csv(os.path.join(testfolder, 'cc_10scen_underInstalls.csv'))

:) Saved Input and Output Dataframes

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2240: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)


scennames_labels2=sim2.scenario.keys()


cumu_installs2 = cc_cumu2.filter(like='newInstalled')
total_installed2 = cumu_installs2.loc[2100]
total_installed2.index= scennames_labels2

plt.bar(scennames_labels2, total_installed2/1e6)
#plt.xticks(rotation=90)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Installs with Replacements')


cumu_area_deployed2 = pd.DataFrame()

for scen in sim2.scenario.keys():
    area_deploy = sim2.scenario[scen].dataOut_m['Area'].cumsum()
    cumu_area_deployed2 = pd.concat([cumu_area_deployed2,area_deploy], axis=1)
    
cumu_area_deployed2.columns = sim2.scenario.keys()
cumu_area_deployed2_total = cumu_area_deployed2.iloc[-1,:]
#cumu_area_deployed

plt.bar(scennames_labels2, cumu_area_deployed2.iloc[-1,:]/1e12)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed Area [m2 1e12]')
plt.title('Cumulative Area Deployed with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Area Deployed with Replacements')


cumu_virgin_module2 = cc_cumu2.filter(like='VirginStock_Module')
virgin2 = cumu_virgin_module2.loc[2100]
virgin2.index= scennames_labels2

plt.bar(scennames_labels2, virgin2/1e9)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
#plt.xticks(rotation=90)

Text(0, 0.5, 'Virgin Material Requirements\n[billion tonnes]')


cumu_lifecycle_wastes2 = cc_cumu2.filter(like='WasteAll_Module')
wastes2 = cumu_lifecycle_wastes2.loc[2100]
wastes2.index= scennames_labels2

plt.bar(scennames_labels2, wastes2/1e9)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[billion tonnes]')

Text(0, 0.5, 'Lifecycle Wastes\n[billion tonnes]')


e_annual_sumDemands2 = energy_demands2.filter(like='demand_total')
e_annual_sumDemands_cumu2 = e_annual_sumDemands2.cumsum()
cumu_e_demands2 = e_annual_sumDemands_cumu2.loc[2100]
cumu_e_demands2.index= scennames_labels2

plt.bar(scennames_labels2, cumu_e_demands2/1e12)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')

Text(0, 0.5, 'Cumulative Energy Demands\n[TWh]')


energyGen_cumu2 = energyGen2.cumsum()
energyGen_cumu2.columns = e_annual_sumDemands_cumu2.columns = scennames_labels2
netEnergy_cumu2 = energyGen_cumu2.loc[[2100]] - e_annual_sumDemands_cumu2.loc[[2100]]
#netEnergy_cumu.index= scennames_labels

plt.bar(scennames_labels2, netEnergy_cumu2.loc[2100]/1e12)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')

Text(0, 0.5, 'Cumulative Net Energy [TWh]')


netEnergy_cumu_norm2 = netEnergy_cumu2/netEnergy_cumu2.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall2 = netEnergy_cumu_norm2-1

plt.bar(scennames_labels2, netEnergy_cumu_norm_waterfall2.loc[2100])
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)
plt.xticks(rotation=90)

([0, 1, 2, 3, 4, 5, 6],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


energyBalance_allyears2 = energyGen_cumu2/e_annual_sumDemands_cumu2
energyBalance2 = energyBalance_allyears2.loc[2100]
energyBalance2.index = scennames_labels2

plt.bar(scennames_labels2, energyBalance2)
plt.title('Energy Balance')
plt.ylabel('Unitless')

Text(0, 0.5, 'Unitless')


discussTable2 = pd.concat([total_installed2,cumu_area_deployed2_total,virgin2,wastes2,
                           cumu_e_demands2, netEnergy_cumu2.loc[2100].T, energyBalance2],
                         axis=1, keys=['replacements','area','virgin','wastes','energydemand','netenergy','energybalance'])
#discussTable.to_csv(os.path.join(testfolder,'discussiontable.csv'))
discussTable2


discussTable_all = pd.concat([discussTable,discussTable2], axis=0)
discussTable_all


discussTable_norm = (discussTable_all/discussTable.loc['PV_ICE'])*100-100
discussTable_norm


#load in a baseline and materials for modification
sim3 = PV_ICE.Simulation(name='sim3', path=testfolder)

sim3.createScenario(name='PV_ICE', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim3.scenario['PV_ICE'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


mod_circ_vars = ['mod_EOL_collection_eff', 'mod_EOL_pg4_recycled', 'mod_EOL_pb4_recycled']
mat_circ_vars = ['mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff', 'mat_MFG_scrap_Recycled_into_HQ',
                 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 'mat_PG4_Recycling_target', 'mat_Recycling_yield',
                 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG']


#range of circularity / closed loop vs downcycled


sim3.createScenario(name='circ_high', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim3.scenario['circ_high'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
for var in range(0,len(mod_circ_vars)):
    sim3.modifyScenario('circ_high', mod_circ_vars[var], 100.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim3.scenario['circ_high'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],100.0, start_year=2022) #

#-----------------------------------------------------------------------------------------------------------

sim1.createScenario(name='circ_low', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['circ_low'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
for var in range(0,len(mod_circ_vars)):
    sim1.modifyScenario('circ_low', mod_circ_vars[var],0.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim1.scenario['circ_low'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],0.0, start_year=2022) #


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim1.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE glass : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silicon : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silver : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE copper : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  life_high
life_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
life_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
life_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
life_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
life_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
life_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
life_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
life_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  life_low
life_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
life_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
life_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
life_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
life_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
life_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
life_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
life_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_high
eff_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_high_bifi
eff_high_bifi glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_low
eff_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_high
circ_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_low
circ_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)
global_projection.iloc[-1,:]

#fig, ax1 = plt.subplots()

#ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
#ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
#ax1.set_ylim(0,90)
#ax1.set_xlim(2000,2100)
#ax2 = ax1.twinx()
#ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
#ax2.set_ylabel('Annual Installations [TW]')
#ax2.set_ylim(0,5)
#plt.show()

#fig.savefig('energyresults-deployment.png', dpi=300, bbox_inches='tight')

World_cum              85966000.0
World_annual_[MWdc]      219320.0
Name: 2100, dtype: float64


#deployment projection for all scenarios
sim1.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)


UnderInstall_df = pd.DataFrame()


for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim1.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Effective_Capacity_[W]'][row])/1e6)  # MWATTS
        sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        UnderInstall_df.loc[row,scen] = Under_Installment #save the underinstallment as df
        #calculate flows for that scenario with it's bifi factor and modified weibull
        sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

sim1.calculateEnergyFlow()

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
life_high
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_high
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
life_low
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_low
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
eff_high
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
eff_high_bifi
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high_bifi
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_1508\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

	year	new_Installed_Capacity_[MW]	mod_eff	mod_reliability_t50	mod_reliability_t90	mod_degradation	mod_lifetime	mod_MFG_eff	mod_Repair	mod_MerchantTail	...	mod_EOL_pg1_landfill	mod_EOL_pg2_stored	mod_EOL_pg3_reMFG	mod_EOL_pg4_recycled	mod_EOL_reMFG_yield	mod_EOL_sp_reMFG_recycle	mod_EOL_pb1_landfill	mod_EOL_pb2_stored	mod_EOL_pb3_reMFG	mod_EOL_pb4_recycled
0	2000	808.669	13.420000	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
1	2001	284.317	13.600000	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
2	2002	344.073	13.722222	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
3	2003	527.971	13.844444	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
4	2004	1086.949	13.966667	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	2096	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
97	2097	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
98	2098	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
99	2099	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
100	2100	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0

	eff_high_mod_MFG	eff_high_mod_Install	eff_high_mod_OandM	eff_high_mod_Repair	eff_high_mod_Demount	eff_high_mod_Store	eff_high_mod_Resell_Certify	eff_high_mod_ReMFG_Disassembly	eff_high_mod_Recycle_Crush	eff_high_backsheet_mat_extraction	...	eff_high_bifi_backsheet_mat_Recycled_HQ_elec	eff_high_bifi_e_demand_total	eff_high_backsheet_mat_MFG_virgin_fuel	eff_high_backsheet_mat_MFGScrap_HQ_fuel	eff_high_backsheet_mat_Landfill_fuel	eff_high_backsheet_mat_Recycled_HQ_fuel	eff_high_bifi_backsheet_mat_MFG_virgin_fuel	eff_high_bifi_backsheet_mat_MFGScrap_HQ_fuel	eff_high_bifi_backsheet_mat_Landfill_fuel	eff_high_bifi_backsheet_mat_Recycled_HQ_fuel
year
2000	5.595432e+10	5.724557e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	0.000000e+00	2.361798e+10	...	0.0	8.782771e+10	1.243623e+10	0.0	0.0	0.0	1.131697e+10	0.0	0.0	0.0
2001	7.599869e+10	7.752961e+07	0.0	0.0	3.657513e-01	0.0	0.0	0.0	1.547705e+00	3.224195e+10	...	0.0	1.203906e+11	1.697724e+10	0.0	0.0	0.0	1.555949e+10	0.0	0.0	0.0
2002	1.003605e+11	1.019176e+08	0.0	0.0	2.862575e+01	0.0	0.0	0.0	1.211321e+02	4.291771e+10	...	0.0	1.594725e+11	2.259865e+10	0.0	0.0	0.0	2.070531e+10	0.0	0.0	0.0
2003	1.376869e+11	1.388866e+08	0.0	0.0	3.744197e+02	0.0	0.0	0.0	1.584386e+03	5.956614e+10	...	0.0	2.198089e+11	3.136501e+10	0.0	0.0	0.0	2.872989e+10	0.0	0.0	0.0
2004	2.141538e+11	2.138242e+08	0.0	0.0	2.361542e+03	0.0	0.0	0.0	9.993052e+03	9.421588e+10	...	0.0	3.440825e+11	4.961009e+10	0.0	0.0	0.0	4.540641e+10	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2096	2.084757e+15	5.842628e+12	0.0	0.0	2.900146e+12	0.0	0.0	0.0	1.227219e+13	3.082694e+15	...	0.0	6.795773e+15	1.623216e+15	0.0	0.0	0.0	1.490753e+15	0.0	0.0	0.0
2097	2.106728e+15	5.904238e+12	0.0	0.0	2.949143e+12	0.0	0.0	0.0	1.247953e+13	3.114726e+15	...	0.0	6.869433e+15	1.640083e+15	0.0	0.0	0.0	1.506818e+15	0.0	0.0	0.0
2098	2.130099e+15	5.969776e+12	0.0	0.0	3.002774e+12	0.0	0.0	0.0	1.270647e+13	3.148800e+15	...	0.0	6.945433e+15	1.658025e+15	0.0	0.0	0.0	1.523389e+15	0.0	0.0	0.0
2099	2.154891e+15	6.039297e+12	0.0	0.0	3.061103e+12	0.0	0.0	0.0	1.295330e+13	3.184944e+15	...	0.0	7.026343e+15	1.677057e+15	0.0	0.0	0.0	1.541029e+15	0.0	0.0	0.0
2100	2.181460e+15	6.113803e+12	0.0	0.0	3.125324e+12	0.0	0.0	0.0	1.322506e+13	3.223680e+15	...	0.0	7.113194e+15	1.697454e+15	0.0	0.0	0.0	1.559962e+15	0.0	0.0	0.0

	replacements	area	virgin	wastes	energydemand	netenergy	energybalance
PV_ICE	1.906612e+08	7.728509e+11	1.008632e+10	5.139612e+09	9.290967e+15	7.178739e+18	773.657878
life_high	1.732992e+08	7.033519e+11	9.180273e+09	4.394466e+09	8.461753e+15	7.179568e+18	849.472856
life_low	2.593475e+08	1.047702e+12	1.366772e+10	8.777769e+09	1.257411e+16	7.175456e+18	571.653296
eff_high	1.906612e+08	6.435582e+11	8.398524e+09	4.276345e+09	7.740063e+15	7.180290e+18	928.678411
eff_high_bifi	1.752295e+08	5.914535e+11	7.718548e+09	3.929078e+09	7.113194e+15	7.840880e+18	1103.300845
eff_low	1.906612e+08	9.678296e+11	1.263162e+10	6.447753e+09	1.163054e+16	7.176399e+18	618.030672
circ_high	1.906612e+08	7.728509e+11	4.746508e+09	3.423740e+06	8.341473e+15	7.179688e+18	861.721883
circ_low	1.906612e+08	7.728509e+11	1.011723e+10	5.374146e+09	9.275074e+15	7.178755e+18	774.983528

	replacements	area	virgin	wastes	energydemand	netenergy	energybalance
PV_ICE	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
life_high	-9.106160e+00	-8.992551	-8.982934	-14.498109	-8.924944	0.011551	9.799548
life_low	3.602537e+01	35.563336	35.507452	70.786595	35.336905	-0.045734	-26.110324
eff_high	-4.263256e-14	-16.729327	-16.733513	-16.796343	-16.692596	0.021604	20.037350
eff_high_bifi	-8.093759e+00	-23.471200	-23.475082	-23.553026	-23.439680	9.223640	42.608364
eff_low	-2.842171e-14	25.228496	25.235163	25.452119	25.181146	-0.032590	-20.115766
circ_high	0.000000e+00	0.000000	-52.941136	-99.933385	-10.219539	0.013226	11.382810
circ_low	0.000000e+00	0.000000	0.306455	4.563261	-0.171055	0.000221	0.171348

	replacements	area	virgin	wastes	energydemand	netenergy	energybalance
PV_ICE	1.906612e+08	7.728509e+11	1.008632e+10	5.139612e+09	9.290967e+15	7.178739e+18	773.657878
good_eff_life	1.590911e+08	5.376261e+11	7.016802e+09	3.354618e+09	6.470979e+15	7.839164e+18	1212.434123
good_eff_circ	1.752295e+08	5.914535e+11	3.636995e+09	3.138244e+06	6.386451e+15	7.841606e+18	1228.850313
good_life_circ	1.732992e+08	7.033519e+11	4.608957e+09	3.423740e+06	7.641676e+15	7.180388e+18	940.635216
bad_life_eff	2.593475e+08	1.311428e+12	1.710881e+10	1.099588e+10	1.573488e+16	7.172295e+18	456.821410
bad_eff_circ	1.906612e+08	7.728509e+11	1.011723e+10	5.374146e+09	9.275074e+15	7.178755e+18	774.983528
bad_life_circ	2.593475e+08	1.047702e+12	1.371424e+10	9.132573e+09	1.254665e+16	7.175483e+18	572.904358

Energy Analysis Sensitivity of Lifetime, Efficiency, and Material Circularity¶

https://www.learnui.design/tools/data-color-picker.html#palette ¶

color pallette - modify here for all graphs below¶

Create Sensitivity Scenarios¶

Life¶

Efficiency¶

Material Circularity¶

Calculate Mass Flow: Identical Installs¶

Calculate Flows: Capacity Compensation¶

Fix the index to be years¶

Best Combos and Worst Combos¶

Good Combos¶

Bad Combos¶

Calculate Mass Flow: Identical Installs¶

Calculate Flows: Capacity Compensation¶

Sensitivity of Energy to Circularity¶

Energy Analysis Sensitivity of Lifetime, Efficiency, and Material Circularity¶

https://www.learnui.design/tools/data-color-picker.html#palette¶

color pallette - modify here for all graphs below¶

Create Sensitivity Scenarios¶

Life¶

Efficiency¶

Material Circularity¶

Calculate Mass Flow: Identical Installs¶

Calculate Flows: Capacity Compensation¶

Fix the index to be years¶

Best Combos and Worst Combos¶

Good Combos¶

Bad Combos¶

Calculate Mass Flow: Identical Installs¶

Calculate Flows: Capacity Compensation¶

Sensitivity of Energy to Circularity¶

https://www.learnui.design/tools/data-color-picker.html#palette ¶